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A document clustering and tagging system serves as an intelligent solution for managing extensive collections of text by automatically grouping related documents and assigning them suitable tags. The system employs various machine learning strategies, including hierarchical and k-means clustering, along with deep learning models, to evaluate and compare the semantic content of documents. Techniques from natural language processing are used to identify main topics and create descriptive metadata tags, which support efficient organization and quick retrieval of information. By implementing this approach, organizations can greatly improve the process of finding information, streamline the management of knowledge, and enhance decision-making by making large datasets more accessible and easier to search.
Keywords:
Document clustering, Tagging system, Machine learning, Information retrieval.
Cite Article:
"A survey on document clustering and tagging system
", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b309-b314, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506138.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator